ScholarWorks@UMassAmherst
We are now able to accept submissions directly in ScholarWorks. For submissions that are not doctoral dissertations or masters theses, please log in with your NetID, click the + (plus) in to the top left corner, and select the Submit Research option.
Graduate students: Directions for submissions can be found in this guide. Please email scholarworks@library.umass.edu if you have any questions.
Request forms for campus-only items are functional. If you do not receive a reply to a submitted request, please email scholarworks@library.umass.edu.
This site is still under construction, please see our ScholarWorks guide for updates.
Recent Submissions
Person Publication Can COVID-19 Be a Turning Point for More Sustainable Trends? Exploring Changes in Recycling Discourse Pre- and During the COVID-19 Pandemic in South Korea(2024)Since the onset of COVID-19, concerns have risen regarding the environmental impact of increased waste generation. Of particular concern is the surge in plastic usage, prompting a call for enhanced recycling practices. This study investigates the impact of the COVID-19 pandemic on recycling discourse in South Korea by analyzing news media coverage on recycling and reuse based on framing theory. Employing Python and R, a total of 24,928 news articles containing keywords (i.e., recycling and reuse) both pre- and during the COVID-19 pandemic were collected and analyzed. The results of computational analyses, such as topic modeling, semantic network analysis, and framing analysis, show that while an initial spike in COVID-19 cases correlated with heightened recycling-related coverage, this trend gradually waned as the pandemic persisted. The results also revealed shifts in keywords, frames, and framing functions over time. Notably, major frames expanded during the pandemic to incorporate COVID-19-related topics and concrete recycling efforts. Throughout, the primary framing function remained prognostic. This research provides valuable insights into the evolving environmental discourse amidst a global pandemic.Publication Comparative Analysis of Machine Learning Models for Predicting Viscosity in Tri-n-Butyl Phosphate Mixtures Using Experimental Data(2024)Tri-n-butyl phosphate (TBP) is essential in the chemical industry for dissolving and purifying various inorganic acids and metals, especially in hydrometallurgical processes. Recent advancements suggest that machine learning can significantly improve the prediction of TBP mixture viscosities, saving time and resources while minimizing exposure to toxic solvents. This study evaluates the effectiveness of five machine learning algorithms for automating TBP mixture viscosity prediction. Using 511 measurements collected across different compositions and temperatures, the neural network (NN) model proved to be the most accurate, achieving a Mean Squared Error (MSE) of 0.157% and an adjusted R2 (a measure of how well the model predicts the variability of the outcome) of 99.72%. The NN model was particularly effective in predicting the viscosity of TBP + ethylbenzene mixtures, with a minimal deviation margin of 0.049%. These results highlight the transformative potential of machine learning to enhance the efficiency and precision of hydrometallurgical processes involving TBP mixtures, while also reducing operational risks.Publication PERFORMANCE OF ADHESIVE STEEL-TO-STEEL CONNECTIONS UNDER PROLONGED LOADING(2025-05)As construction moves towards modularization and prefabrication, adhered joints have become increasingly prevalent across civil infrastructure, particularly in automotive, naval, aerospace, engineered wood, and concrete industries. Adhered joints offer the benefits of easier application and continuity of the connection without inducing additional residual stress in the connections in the ways that traditional bolted and welded connections do. However, these benefits are largely ignored in the steel industry, in part due the lack of research on the behavior of adhesives in steel connections. This thesis discusses the success of structural adhesives in heavy structural settings, highlighting the opportunities for adhered joints in heavy structural connections, through an extensive review of test standards, research, and commercially available structural adhesives. From this review, preliminary statistical-analysis based design objectives for adhesive steel-to-steel connections were established. An experimental program was designed to evaluate the performance and behavior of five adhesives in steel-to-steel connections under a variety of sustained loads. Many of the tests in this series have been under load for over a year, thus, creep curves have been fit to the measured creep strains. Statistical analysis of these creep curves indicates the slope of the secondary creep region for all tests is near-zero, and that behavior at low and intermediate load levels is distinct from that of higher load levels.Publication Universalizing Dark Heritage: Edward Berger’s All Quiet on the Western Front (2022)(2025-05)In this study, I analyze Edward Berger’s 2022 film All Quiet on the Western Front as a “heritage film.” Scholars use this analytical lens to uncover the ways in which national cinemas portray history and thus partake in the construction of a national heritage. I argue that All Quiet on the Western Front is a special case of the heritage film, for two reasons. First, the film constructs heritage by depicting a history of trauma, thus creating what scholars have termed “dark heritage.” Second, the film does not construct a strictly national heritage; instead, it de-emphasizes the national perspective and universalizes the heritage of WWI. The film depicts the events of the Western Front as a traumatic part of history that concerns all of humanity, thus constructing a universal human heritage. In my study, I first define the terms “heritage,” “dark heritage,” and “heritage film.” Then, I analyze key scenes to show how All Quiet on the Front uses narrative and audiovisual tools to universalize the heritage of WWI.
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